
We greatly reduce the instability of GAN training by decoupling the search of generator and discriminator into two stages. To avoid mode collapse, we propose a general weight-resetting strategy in stage-2 to obtain fair and unbiased evaluation of different discriminators.

With the weight-sharing and low-fidelity strategies, EAGAN can efficiently search GANs within 1.2 GPU days on the CIFAR-10 dataset.

EAGAN can discover superior GAN architectures on the CIFAR-10 dataset. The searched GAN has advanced transferability and surpasses all previous NAS-searched GANs on the STL-10 dataset [1]}.

